Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations1048532
Missing cells2648376
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 GiB
Average record size in memory1.0 KiB

Variable types

Numeric8
Categorical6
Text9
DateTime1

Alerts

Acceptance Status is highly overall correlated with Acceptance to Installation Days and 6 other fieldsHigh correlation
Acceptance to Installation Days is highly overall correlated with Acceptance StatusHigh correlation
Approval to Vendor Selection Days is highly overall correlated with Acceptance StatusHigh correlation
Inspection to Subsidy Redeemed Days is highly overall correlated with Acceptance StatusHigh correlation
Installation to Inspection Days is highly overall correlated with Acceptance StatusHigh correlation
Registration to Approval Days is highly overall correlated with Acceptance StatusHigh correlation
Subsidy Redeemed to Released Days is highly overall correlated with Acceptance StatusHigh correlation
Vendor Selection to Acceptance Days is highly overall correlated with Acceptance StatusHigh correlation
Registration to Approval Days has 202247 (19.3%) missing values Missing
Approval to Vendor Selection Days has 233977 (22.3%) missing values Missing
Vendor Selection to Acceptance Days has 284452 (27.1%) missing values Missing
Acceptance to Installation Days has 356903 (34.0%) missing values Missing
Installation to Inspection Days has 436530 (41.6%) missing values Missing
Inspection to Subsidy Redeemed Days has 523090 (49.9%) missing values Missing
Subsidy Redeemed to Released Days has 611177 (58.3%) missing values Missing
Application Number has unique values Unique

Reproduction

Analysis started2025-03-27 05:09:23.198827
Analysis finished2025-03-27 05:10:26.959283
Duration1 minute and 3.76 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Application Number
Real number (ℝ)

Unique 

Distinct1048532
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54955118
Minimum10000005
Maximum99999993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-27T10:40:27.079738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10000005
5-th percentile14482858
Q132490250
median54924249
Q377438318
95-th percentile95473603
Maximum99999993
Range89999988
Interquartile range (IQR)44948068

Descriptive statistics

Standard deviation25973624
Coefficient of variation (CV)0.4726334
Kurtosis-1.199105
Mean54955118
Median Absolute Deviation (MAD)22474760
Skewness0.0017519428
Sum5.76222 × 1013
Variance6.7462916 × 1014
MonotonicityNot monotonic
2025-03-27T10:40:27.224399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87559218 1
 
< 0.1%
77442871 1
 
< 0.1%
46760652 1
 
< 0.1%
50016740 1
 
< 0.1%
25843214 1
 
< 0.1%
48994393 1
 
< 0.1%
63253297 1
 
< 0.1%
54760876 1
 
< 0.1%
82219819 1
 
< 0.1%
73776238 1
 
< 0.1%
Other values (1048522) 1048522
> 99.9%
ValueCountFrequency (%)
10000005 1
< 0.1%
10000035 1
< 0.1%
10000114 1
< 0.1%
10000125 1
< 0.1%
10000176 1
< 0.1%
10000184 1
< 0.1%
10000288 1
< 0.1%
10000342 1
< 0.1%
10000536 1
< 0.1%
10000601 1
< 0.1%
ValueCountFrequency (%)
99999993 1
< 0.1%
99999883 1
< 0.1%
99999822 1
< 0.1%
99999796 1
< 0.1%
99999652 1
< 0.1%
99999627 1
< 0.1%
99999553 1
< 0.1%
99999306 1
< 0.1%
99999282 1
< 0.1%
99999280 1
< 0.1%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Male
576349 
Female
461887 
Other
 
10296

Length

Max length6
Median length4
Mean length4.8908359
Min length4

Characters and Unicode

Total characters5128198
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 576349
55.0%
Female 461887
44.1%
Other 10296
 
1.0%

Length

2025-03-27T10:40:27.357628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-27T10:40:27.441031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 576349
55.0%
female 461887
44.1%
other 10296
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 1510419
29.5%
a 1038236
20.2%
l 1038236
20.2%
M 576349
 
11.2%
F 461887
 
9.0%
m 461887
 
9.0%
O 10296
 
0.2%
t 10296
 
0.2%
h 10296
 
0.2%
r 10296
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5128198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1510419
29.5%
a 1038236
20.2%
l 1038236
20.2%
M 576349
 
11.2%
F 461887
 
9.0%
m 461887
 
9.0%
O 10296
 
0.2%
t 10296
 
0.2%
h 10296
 
0.2%
r 10296
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5128198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1510419
29.5%
a 1038236
20.2%
l 1038236
20.2%
M 576349
 
11.2%
F 461887
 
9.0%
m 461887
 
9.0%
O 10296
 
0.2%
t 10296
 
0.2%
h 10296
 
0.2%
r 10296
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5128198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1510419
29.5%
a 1038236
20.2%
l 1038236
20.2%
M 576349
 
11.2%
F 461887
 
9.0%
m 461887
 
9.0%
O 10296
 
0.2%
t 10296
 
0.2%
h 10296
 
0.2%
r 10296
 
0.2%

State/UT
Categorical

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
Gujarat
197845 
Maharashtra
111407 
Uttar Pradesh
99415 
Rajasthan
83064 
Karnataka
60533 
Other values (31)
496268 

Length

Max length40
Median length17
Mean length9.9951799
Min length3

Characters and Unicode

Total characters10480266
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMizoram
2nd rowMaharashtra
3rd rowMadhya Pradesh
4th rowAndhra Pradesh
5th rowBihar

Common Values

ValueCountFrequency (%)
Gujarat 197845
18.9%
Maharashtra 111407
 
10.6%
Uttar Pradesh 99415
 
9.5%
Rajasthan 83064
 
7.9%
Karnataka 60533
 
5.8%
Telangana 59847
 
5.7%
Andhra Pradesh 58682
 
5.6%
Madhya Pradesh 39760
 
3.8%
Tamil Nadu 37071
 
3.5%
Punjab 21796
 
2.1%
Other values (26) 279112
26.6%

Length

2025-03-27T10:40:27.558535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 218426
15.4%
gujarat 197845
14.0%
maharashtra 111407
 
7.9%
uttar 99415
 
7.0%
rajasthan 83064
 
5.9%
karnataka 60533
 
4.3%
telangana 59847
 
4.2%
andhra 58682
 
4.1%
and 43185
 
3.0%
madhya 39760
 
2.8%
Other values (37) 444759
31.4%

Most occurring characters

ValueCountFrequency (%)
a 2642015
25.2%
r 1053045
 
10.0%
h 834529
 
8.0%
t 733710
 
7.0%
n 509870
 
4.9%
s 509351
 
4.9%
d 502715
 
4.8%
368391
 
3.5%
e 367569
 
3.5%
u 318701
 
3.0%
Other values (33) 2640370
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10480266
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2642015
25.2%
r 1053045
 
10.0%
h 834529
 
8.0%
t 733710
 
7.0%
n 509870
 
4.9%
s 509351
 
4.9%
d 502715
 
4.8%
368391
 
3.5%
e 367569
 
3.5%
u 318701
 
3.0%
Other values (33) 2640370
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10480266
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2642015
25.2%
r 1053045
 
10.0%
h 834529
 
8.0%
t 733710
 
7.0%
n 509870
 
4.9%
s 509351
 
4.9%
d 502715
 
4.8%
368391
 
3.5%
e 367569
 
3.5%
u 318701
 
3.0%
Other values (33) 2640370
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10480266
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2642015
25.2%
r 1053045
 
10.0%
h 834529
 
8.0%
t 733710
 
7.0%
n 509870
 
4.9%
s 509351
 
4.9%
d 502715
 
4.8%
368391
 
3.5%
e 367569
 
3.5%
u 318701
 
3.0%
Other values (33) 2640370
25.2%
Distinct733
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.5 MiB
2025-03-27T10:40:27.850239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length22
Mean length8.4649806
Min length3

Characters and Unicode

Total characters8875803
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLunglei
2nd rowNanded
3rd rowSehore
4th rowNellore
5th rowKhagaria
ValueCountFrequency (%)
north 18529
 
1.5%
south 17380
 
1.4%
west 11565
 
0.9%
east 11374
 
0.9%
mumbai 10498
 
0.9%
kheda 10128
 
0.8%
junagadh 10038
 
0.8%
kachchh 9990
 
0.8%
mahisagar 9771
 
0.8%
jamnagar 9638
 
0.8%
Other values (736) 1101375
90.3%
2025-03-27T10:40:28.163041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1795702
20.2%
r 729274
 
8.2%
h 554249
 
6.2%
i 532512
 
6.0%
n 517513
 
5.8%
u 427158
 
4.8%
d 340757
 
3.8%
o 321708
 
3.6%
l 314445
 
3.5%
t 268599
 
3.0%
Other values (44) 3073886
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8875803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1795702
20.2%
r 729274
 
8.2%
h 554249
 
6.2%
i 532512
 
6.0%
n 517513
 
5.8%
u 427158
 
4.8%
d 340757
 
3.8%
o 321708
 
3.6%
l 314445
 
3.5%
t 268599
 
3.0%
Other values (44) 3073886
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8875803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1795702
20.2%
r 729274
 
8.2%
h 554249
 
6.2%
i 532512
 
6.0%
n 517513
 
5.8%
u 427158
 
4.8%
d 340757
 
3.8%
o 321708
 
3.6%
l 314445
 
3.5%
t 268599
 
3.0%
Other values (44) 3073886
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8875803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1795702
20.2%
r 729274
 
8.2%
h 554249
 
6.2%
i 532512
 
6.0%
n 517513
 
5.8%
u 427158
 
4.8%
d 340757
 
3.8%
o 321708
 
3.6%
l 314445
 
3.5%
t 268599
 
3.0%
Other values (44) 3073886
34.6%

RWA/Residential
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.3 MiB
Residential
831968 
RWA
216564 

Length

Max length11
Median length11
Mean length9.3476785
Min length3

Characters and Unicode

Total characters9801340
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResidential
2nd rowResidential
3rd rowResidential
4th rowResidential
5th rowRWA

Common Values

ValueCountFrequency (%)
Residential 831968
79.3%
RWA 216564
 
20.7%

Length

2025-03-27T10:40:28.377985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-27T10:40:28.422769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
residential 831968
79.3%
rwa 216564
 
20.7%

Most occurring characters

ValueCountFrequency (%)
e 1663936
17.0%
i 1663936
17.0%
R 1048532
10.7%
s 831968
8.5%
d 831968
8.5%
n 831968
8.5%
t 831968
8.5%
a 831968
8.5%
l 831968
8.5%
W 216564
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9801340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1663936
17.0%
i 1663936
17.0%
R 1048532
10.7%
s 831968
8.5%
d 831968
8.5%
n 831968
8.5%
t 831968
8.5%
a 831968
8.5%
l 831968
8.5%
W 216564
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9801340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1663936
17.0%
i 1663936
17.0%
R 1048532
10.7%
s 831968
8.5%
d 831968
8.5%
n 831968
8.5%
t 831968
8.5%
a 831968
8.5%
l 831968
8.5%
W 216564
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9801340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1663936
17.0%
i 1663936
17.0%
R 1048532
10.7%
s 831968
8.5%
d 831968
8.5%
n 831968
8.5%
t 831968
8.5%
a 831968
8.5%
l 831968
8.5%
W 216564
 
2.2%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.6 MiB
2025-03-27T10:40:28.638731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length69
Median length58
Mean length47.640517
Min length10

Characters and Unicode

Total characters49952607
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational Thermal Power Corporation (NTPC)
2nd rowMaharashtra State Electricity Distribution Company Limited (MSEDCL)
3rd rowMadhya Pradesh Poorv Kshetra Vidyut Vitaran Company Limited
4th rowAndhra Pradesh Central Power Distribution Company Limited
5th rowNorth Bihar Power Distribution Company Limited (NBPDCL)
ValueCountFrequency (%)
limited 704098
 
11.0%
company 441006
 
6.9%
power 323812
 
5.1%
corporation 255700
 
4.0%
distribution 221209
 
3.5%
electricity 193834
 
3.0%
of 184636
 
2.9%
vidyut 167836
 
2.6%
vij 152926
 
2.4%
gujarat 152926
 
2.4%
Other values (125) 3598028
56.3%
2025-03-27T10:40:28.985363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5347479
 
10.7%
i 4251037
 
8.5%
a 3974765
 
8.0%
t 3296068
 
6.6%
r 3060770
 
6.1%
o 2650605
 
5.3%
n 2288304
 
4.6%
e 2274327
 
4.6%
d 1829940
 
3.7%
m 1712073
 
3.4%
Other values (41) 19267239
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49952607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5347479
 
10.7%
i 4251037
 
8.5%
a 3974765
 
8.0%
t 3296068
 
6.6%
r 3060770
 
6.1%
o 2650605
 
5.3%
n 2288304
 
4.6%
e 2274327
 
4.6%
d 1829940
 
3.7%
m 1712073
 
3.4%
Other values (41) 19267239
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49952607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5347479
 
10.7%
i 4251037
 
8.5%
a 3974765
 
8.0%
t 3296068
 
6.6%
r 3060770
 
6.1%
o 2650605
 
5.3%
n 2288304
 
4.6%
e 2274327
 
4.6%
d 1829940
 
3.7%
m 1712073
 
3.4%
Other values (41) 19267239
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49952607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5347479
 
10.7%
i 4251037
 
8.5%
a 3974765
 
8.0%
t 3296068
 
6.6%
r 3060770
 
6.1%
o 2650605
 
5.3%
n 2288304
 
4.6%
e 2274327
 
4.6%
d 1829940
 
3.7%
m 1712073
 
3.4%
Other values (41) 19267239
38.6%

Acceptance Status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.0 MiB
Accepted
876634 
Rejected
171898 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8388256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccepted
2nd rowAccepted
3rd rowAccepted
4th rowAccepted
5th rowAccepted

Common Values

ValueCountFrequency (%)
Accepted 876634
83.6%
Rejected 171898
 
16.4%

Length

2025-03-27T10:40:29.079545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-27T10:40:29.127716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
accepted 876634
83.6%
rejected 171898
 
16.4%

Most occurring characters

ValueCountFrequency (%)
e 2268962
27.0%
c 1925166
23.0%
d 1048532
12.5%
t 1048532
12.5%
p 876634
 
10.5%
A 876634
 
10.5%
R 171898
 
2.0%
j 171898
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8388256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2268962
27.0%
c 1925166
23.0%
d 1048532
12.5%
t 1048532
12.5%
p 876634
 
10.5%
A 876634
 
10.5%
R 171898
 
2.0%
j 171898
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8388256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2268962
27.0%
c 1925166
23.0%
d 1048532
12.5%
t 1048532
12.5%
p 876634
 
10.5%
A 876634
 
10.5%
R 171898
 
2.0%
j 171898
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8388256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2268962
27.0%
c 1925166
23.0%
d 1048532
12.5%
t 1048532
12.5%
p 876634
 
10.5%
A 876634
 
10.5%
R 171898
 
2.0%
j 171898
 
2.0%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.0 MiB
3 - 4 KW
734297 
4 - 5 KW
154161 
5 - 6 KW
77084 
2 - 3 KW
 
58091
Above 6 KW
 
20914

Length

Max length10
Median length8
Mean length8.039892
Min length8

Characters and Unicode

Total characters8430084
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3 - 4 KW
2nd row3 - 4 KW
3rd row3 - 4 KW
4th row4 - 5 KW
5th row3 - 4 KW

Common Values

ValueCountFrequency (%)
3 - 4 KW 734297
70.0%
4 - 5 KW 154161
 
14.7%
5 - 6 KW 77084
 
7.4%
2 - 3 KW 58091
 
5.5%
Above 6 KW 20914
 
2.0%
1 - 2 KW 3985
 
0.4%

Length

2025-03-27T10:40:29.213884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-27T10:40:29.282418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kw 1048532
25.1%
1027618
24.6%
4 888458
21.3%
3 792388
19.0%
5 231245
 
5.5%
6 97998
 
2.3%
2 62076
 
1.5%
above 20914
 
0.5%
1 3985
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3124682
37.1%
W 1048532
 
12.4%
K 1048532
 
12.4%
- 1027618
 
12.2%
4 888458
 
10.5%
3 792388
 
9.4%
5 231245
 
2.7%
6 97998
 
1.2%
2 62076
 
0.7%
A 20914
 
0.2%
Other values (5) 87641
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8430084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3124682
37.1%
W 1048532
 
12.4%
K 1048532
 
12.4%
- 1027618
 
12.2%
4 888458
 
10.5%
3 792388
 
9.4%
5 231245
 
2.7%
6 97998
 
1.2%
2 62076
 
0.7%
A 20914
 
0.2%
Other values (5) 87641
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8430084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3124682
37.1%
W 1048532
 
12.4%
K 1048532
 
12.4%
- 1027618
 
12.2%
4 888458
 
10.5%
3 792388
 
9.4%
5 231245
 
2.7%
6 97998
 
1.2%
2 62076
 
0.7%
A 20914
 
0.2%
Other values (5) 87641
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8430084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3124682
37.1%
W 1048532
 
12.4%
K 1048532
 
12.4%
- 1027618
 
12.2%
4 888458
 
10.5%
3 792388
 
9.4%
5 231245
 
2.7%
6 97998
 
1.2%
2 62076
 
0.7%
A 20914
 
0.2%
Other values (5) 87641
 
1.0%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 MiB
SkyPower Solar
82321 
BrightSun Power
78633 
GreenSpark Solar
74320 
RadiantSun Energy
74173 
SunWave Energy
70074 
Other values (15)
669011 

Length

Max length27
Median length23
Mean length18.883117
Min length13

Characters and Unicode

Total characters19799552
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreenSpark Solar
2nd rowGreenSpark Solar
3rd rowSolarPeak Innovations
4th rowPowerSun Technologies
5th rowSolarCrest Enterprises

Common Values

ValueCountFrequency (%)
SkyPower Solar 82321
 
7.9%
BrightSun Power 78633
 
7.5%
GreenSpark Solar 74320
 
7.1%
RadiantSun Energy 74173
 
7.1%
SunWave Energy 70074
 
6.7%
SunTech Solar Solutions 65751
 
6.3%
SunRise Renewable Solutions 61272
 
5.8%
SolarHarvest Energy 56970
 
5.4%
SolarPeak Innovations 56690
 
5.4%
InfiniteLight Solar 52410
 
5.0%
Other values (10) 375918
35.9%

Length

2025-03-27T10:40:29.389955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
solar 340492
 
14.8%
energy 232138
 
10.1%
solutions 157944
 
6.9%
power 117904
 
5.1%
systems 105000
 
4.6%
technologies 96350
 
4.2%
skypower 82321
 
3.6%
brightsun 78633
 
3.4%
enterprises 77977
 
3.4%
greenspark 74320
 
3.2%
Other values (19) 931220
40.6%

Most occurring characters

ValueCountFrequency (%)
e 2010478
 
10.2%
n 1740615
 
8.8%
r 1671354
 
8.4%
o 1551297
 
7.8%
S 1490014
 
7.5%
1245767
 
6.3%
a 1196665
 
6.0%
l 1005035
 
5.1%
t 842754
 
4.3%
s 838349
 
4.2%
Other values (26) 6207224
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19799552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2010478
 
10.2%
n 1740615
 
8.8%
r 1671354
 
8.4%
o 1551297
 
7.8%
S 1490014
 
7.5%
1245767
 
6.3%
a 1196665
 
6.0%
l 1005035
 
5.1%
t 842754
 
4.3%
s 838349
 
4.2%
Other values (26) 6207224
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19799552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2010478
 
10.2%
n 1740615
 
8.8%
r 1671354
 
8.4%
o 1551297
 
7.8%
S 1490014
 
7.5%
1245767
 
6.3%
a 1196665
 
6.0%
l 1005035
 
5.1%
t 842754
 
4.3%
s 838349
 
4.2%
Other values (26) 6207224
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19799552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2010478
 
10.2%
n 1740615
 
8.8%
r 1671354
 
8.4%
o 1551297
 
7.8%
S 1490014
 
7.5%
1245767
 
6.3%
a 1196665
 
6.0%
l 1005035
 
5.1%
t 842754
 
4.3%
s 838349
 
4.2%
Other values (26) 6207224
31.4%
Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
Minimum2024-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-27T10:40:29.484224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:29.614618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.6 MiB
2025-03-27T10:40:29.816052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.585284
Min length7

Characters and Unicode

Total characters10050477
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-11-10
2nd row2024-02-13
3rd row2024-04-15
4th row2024-10-03
5th row2024-07-08
ValueCountFrequency (%)
declined 171898
 
16.4%
pending 30349
 
2.9%
2024-07-31 4270
 
0.4%
2024-07-30 4205
 
0.4%
2024-07-29 4184
 
0.4%
2024-07-27 4182
 
0.4%
2024-08-03 4178
 
0.4%
2024-08-02 4172
 
0.4%
2024-08-04 4144
 
0.4%
2024-08-01 4133
 
0.4%
Other values (356) 812817
77.5%
2025-03-27T10:40:30.093442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2184537
21.7%
0 1830490
18.2%
- 1692570
16.8%
4 985968
9.8%
1 750097
 
7.5%
e 374145
 
3.7%
n 232596
 
2.3%
i 202247
 
2.0%
d 202247
 
2.0%
7 197347
 
2.0%
Other values (10) 1398233
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10050477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2184537
21.7%
0 1830490
18.2%
- 1692570
16.8%
4 985968
9.8%
1 750097
 
7.5%
e 374145
 
3.7%
n 232596
 
2.3%
i 202247
 
2.0%
d 202247
 
2.0%
7 197347
 
2.0%
Other values (10) 1398233
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10050477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2184537
21.7%
0 1830490
18.2%
- 1692570
16.8%
4 985968
9.8%
1 750097
 
7.5%
e 374145
 
3.7%
n 232596
 
2.3%
i 202247
 
2.0%
d 202247
 
2.0%
7 197347
 
2.0%
Other values (10) 1398233
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10050477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2184537
21.7%
0 1830490
18.2%
- 1692570
16.8%
4 985968
9.8%
1 750097
 
7.5%
e 374145
 
3.7%
n 232596
 
2.3%
i 202247
 
2.0%
d 202247
 
2.0%
7 197347
 
2.0%
Other values (10) 1398233
13.9%
Distinct361
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.5 MiB
2025-03-27T10:40:30.295348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.4944999
Min length7

Characters and Unicode

Total characters9955287
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-11-21
2nd row2024-03-21
3rd row2024-04-21
4th row2024-10-11
5th row2024-07-14
ValueCountFrequency (%)
declined 171898
 
16.4%
pending 62079
 
5.9%
2024-08-07 4177
 
0.4%
2024-08-08 4151
 
0.4%
2024-08-04 4125
 
0.4%
2024-08-06 4077
 
0.4%
2024-08-11 4063
 
0.4%
2024-08-02 4044
 
0.4%
2024-08-12 4030
 
0.4%
2024-08-09 4017
 
0.4%
Other values (351) 781871
74.6%
2025-03-27T10:40:30.579051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2109080
21.2%
0 1758861
17.7%
- 1629110
16.4%
4 947386
9.5%
1 709376
 
7.1%
e 405875
 
4.1%
n 296056
 
3.0%
i 233977
 
2.4%
d 233977
 
2.4%
8 192026
 
1.9%
Other values (10) 1439563
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9955287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2109080
21.2%
0 1758861
17.7%
- 1629110
16.4%
4 947386
9.5%
1 709376
 
7.1%
e 405875
 
4.1%
n 296056
 
3.0%
i 233977
 
2.4%
d 233977
 
2.4%
8 192026
 
1.9%
Other values (10) 1439563
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9955287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2109080
21.2%
0 1758861
17.7%
- 1629110
16.4%
4 947386
9.5%
1 709376
 
7.1%
e 405875
 
4.1%
n 296056
 
3.0%
i 233977
 
2.4%
d 233977
 
2.4%
8 192026
 
1.9%
Other values (10) 1439563
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9955287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2109080
21.2%
0 1758861
17.7%
- 1629110
16.4%
4 947386
9.5%
1 709376
 
7.1%
e 405875
 
4.1%
n 296056
 
3.0%
i 233977
 
2.4%
d 233977
 
2.4%
8 192026
 
1.9%
Other values (10) 1439563
14.5%
Distinct356
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.3 MiB
2025-03-27T10:40:30.740120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.3500837
Min length7

Characters and Unicode

Total characters9803862
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2024-11-30
2nd row2024-04-15
3rd row2024-04-26
4th row2024-10-16
5th row2024-07-22
ValueCountFrequency (%)
declined 171898
 
16.4%
pending 112554
 
10.7%
2024-08-15 3799
 
0.4%
2024-08-18 3755
 
0.4%
2024-08-19 3754
 
0.4%
2024-08-16 3719
 
0.4%
2024-08-20 3708
 
0.4%
2024-08-14 3702
 
0.4%
2024-08-17 3686
 
0.4%
2024-08-21 3685
 
0.4%
Other values (346) 734272
70.0%
2025-03-27T10:40:30.999580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1973730
20.1%
0 1646747
16.8%
- 1528160
15.6%
4 886741
9.0%
1 667109
 
6.8%
e 456350
 
4.7%
n 397006
 
4.0%
i 284452
 
2.9%
d 284452
 
2.9%
8 185320
 
1.9%
Other values (10) 1493795
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9803862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1973730
20.1%
0 1646747
16.8%
- 1528160
15.6%
4 886741
9.0%
1 667109
 
6.8%
e 456350
 
4.7%
n 397006
 
4.0%
i 284452
 
2.9%
d 284452
 
2.9%
8 185320
 
1.9%
Other values (10) 1493795
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9803862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1973730
20.1%
0 1646747
16.8%
- 1528160
15.6%
4 886741
9.0%
1 667109
 
6.8%
e 456350
 
4.7%
n 397006
 
4.0%
i 284452
 
2.9%
d 284452
 
2.9%
8 185320
 
1.9%
Other values (10) 1493795
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9803862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1973730
20.1%
0 1646747
16.8%
- 1528160
15.6%
4 886741
9.0%
1 667109
 
6.8%
e 456350
 
4.7%
n 397006
 
4.0%
i 284452
 
2.9%
d 284452
 
2.9%
8 185320
 
1.9%
Other values (10) 1493795
15.2%
Distinct349
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.1 MiB
2025-03-27T10:40:31.172418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.1427911
Min length7

Characters and Unicode

Total characters9586509
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2024-12-23
2nd row2024-05-31
3rd row2024-05-16
4th row2024-11-06
5th row2024-08-14
ValueCountFrequency (%)
pending 185005
 
17.6%
declined 171898
 
16.4%
2024-09-10 3548
 
0.3%
2024-09-11 3537
 
0.3%
2024-09-16 3525
 
0.3%
2024-09-07 3518
 
0.3%
2024-09-06 3511
 
0.3%
2024-09-13 3508
 
0.3%
2024-08-31 3487
 
0.3%
2024-09-05 3480
 
0.3%
Other values (339) 663515
63.3%
2025-03-27T10:40:31.453650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1775018
18.5%
0 1485013
15.5%
- 1383258
14.4%
4 798165
8.3%
1 632158
 
6.6%
n 541908
 
5.7%
e 528801
 
5.5%
d 356903
 
3.7%
i 356903
 
3.7%
P 185005
 
1.9%
Other values (10) 1543377
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9586509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1775018
18.5%
0 1485013
15.5%
- 1383258
14.4%
4 798165
8.3%
1 632158
 
6.6%
n 541908
 
5.7%
e 528801
 
5.5%
d 356903
 
3.7%
i 356903
 
3.7%
P 185005
 
1.9%
Other values (10) 1543377
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9586509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1775018
18.5%
0 1485013
15.5%
- 1383258
14.4%
4 798165
8.3%
1 632158
 
6.6%
n 541908
 
5.7%
e 528801
 
5.5%
d 356903
 
3.7%
i 356903
 
3.7%
P 185005
 
1.9%
Other values (10) 1543377
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9586509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1775018
18.5%
0 1485013
15.5%
- 1383258
14.4%
4 798165
8.3%
1 632158
 
6.6%
n 541908
 
5.7%
e 528801
 
5.5%
d 356903
 
3.7%
i 356903
 
3.7%
P 185005
 
1.9%
Other values (10) 1543377
16.1%
Distinct340
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
2025-03-27T10:40:31.626502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.9149668
Min length7

Characters and Unicode

Total characters9347628
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd row2024-06-24
3rd row2024-06-28
4th row2024-11-28
5th row2024-09-02
ValueCountFrequency (%)
pending 264632
25.2%
declined 171898
 
16.4%
2024-10-06 3303
 
0.3%
2024-10-01 3264
 
0.3%
2024-10-04 3257
 
0.3%
2024-10-07 3245
 
0.3%
2024-10-10 3243
 
0.3%
2024-10-03 3221
 
0.3%
2024-10-02 3204
 
0.3%
2024-10-14 3203
 
0.3%
Other values (330) 586062
55.9%
2025-03-27T10:40:31.890676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1573492
16.8%
0 1291123
13.8%
- 1224004
13.1%
n 701162
7.5%
4 696249
7.4%
1 616337
 
6.6%
e 608428
 
6.5%
d 436530
 
4.7%
i 436530
 
4.7%
P 264632
 
2.8%
Other values (10) 1499141
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9347628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1573492
16.8%
0 1291123
13.8%
- 1224004
13.1%
n 701162
7.5%
4 696249
7.4%
1 616337
 
6.6%
e 608428
 
6.5%
d 436530
 
4.7%
i 436530
 
4.7%
P 264632
 
2.8%
Other values (10) 1499141
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9347628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1573492
16.8%
0 1291123
13.8%
- 1224004
13.1%
n 701162
7.5%
4 696249
7.4%
1 616337
 
6.6%
e 608428
 
6.5%
d 436530
 
4.7%
i 436530
 
4.7%
P 264632
 
2.8%
Other values (10) 1499141
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9347628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1573492
16.8%
0 1291123
13.8%
- 1224004
13.1%
n 701162
7.5%
4 696249
7.4%
1 616337
 
6.6%
e 608428
 
6.5%
d 436530
 
4.7%
i 436530
 
4.7%
P 264632
 
2.8%
Other values (10) 1499141
16.0%
Distinct326
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.7 MiB
2025-03-27T10:40:32.048037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.6673063
Min length7

Characters and Unicode

Total characters9087948
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd row2024-07-12
3rd row2024-07-21
4th rowPending
5th row2024-09-25
ValueCountFrequency (%)
pending 351192
33.5%
declined 171898
 
16.4%
2024-10-26 3106
 
0.3%
2024-11-01 3099
 
0.3%
2024-11-02 3095
 
0.3%
2024-11-05 3089
 
0.3%
2024-11-17 3088
 
0.3%
2024-11-18 3079
 
0.3%
2024-11-22 3073
 
0.3%
2024-11-13 3069
 
0.3%
Other values (316) 500744
47.8%
2025-03-27T10:40:32.283378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1367797
15.1%
0 1072852
11.8%
- 1050884
11.6%
n 874282
9.6%
e 694988
7.6%
1 588231
6.5%
4 586717
6.5%
d 523090
 
5.8%
i 523090
 
5.8%
P 351192
 
3.9%
Other values (10) 1454825
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9087948
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1367797
15.1%
0 1072852
11.8%
- 1050884
11.6%
n 874282
9.6%
e 694988
7.6%
1 588231
6.5%
4 586717
6.5%
d 523090
 
5.8%
i 523090
 
5.8%
P 351192
 
3.9%
Other values (10) 1454825
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9087948
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1367797
15.1%
0 1072852
11.8%
- 1050884
11.6%
n 874282
9.6%
e 694988
7.6%
1 588231
6.5%
4 586717
6.5%
d 523090
 
5.8%
i 523090
 
5.8%
P 351192
 
3.9%
Other values (10) 1454825
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9087948
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1367797
15.1%
0 1072852
11.8%
- 1050884
11.6%
n 874282
9.6%
e 694988
7.6%
1 588231
6.5%
4 586717
6.5%
d 523090
 
5.8%
i 523090
 
5.8%
P 351192
 
3.9%
Other values (10) 1454825
16.0%
Distinct310
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 MiB
2025-03-27T10:40:32.469166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.4152768
Min length7

Characters and Unicode

Total characters8823687
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd row2024-09-04
3rd row2024-08-12
4th rowPending
5th row2024-10-15
ValueCountFrequency (%)
pending 439279
41.9%
declined 171898
 
16.4%
2024-12-15 3001
 
0.3%
2024-11-21 3001
 
0.3%
2024-12-09 2993
 
0.3%
2024-12-06 2987
 
0.3%
2024-12-18 2985
 
0.3%
2024-12-13 2980
 
0.3%
2024-12-12 2972
 
0.3%
2024-12-08 2962
 
0.3%
Other values (300) 413474
39.4%
2025-03-27T10:40:32.770483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1157258
13.1%
n 1050456
11.9%
- 874710
9.9%
0 863489
9.8%
e 783075
8.9%
d 611177
6.9%
i 611177
6.9%
1 527149
 
6.0%
4 483310
 
5.5%
P 439279
 
5.0%
Other values (10) 1422607
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8823687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1157258
13.1%
n 1050456
11.9%
- 874710
9.9%
0 863489
9.8%
e 783075
8.9%
d 611177
6.9%
i 611177
6.9%
1 527149
 
6.0%
4 483310
 
5.5%
P 439279
 
5.0%
Other values (10) 1422607
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8823687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1157258
13.1%
n 1050456
11.9%
- 874710
9.9%
0 863489
9.8%
e 783075
8.9%
d 611177
6.9%
i 611177
6.9%
1 527149
 
6.0%
4 483310
 
5.5%
P 439279
 
5.0%
Other values (10) 1422607
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8823687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1157258
13.1%
n 1050456
11.9%
- 874710
9.9%
0 863489
9.8%
e 783075
8.9%
d 611177
6.9%
i 611177
6.9%
1 527149
 
6.0%
4 483310
 
5.5%
P 439279
 
5.0%
Other values (10) 1422607
16.1%

Registration to Approval Days
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)< 0.1%
Missing202247
Missing (%)19.3%
Infinite0
Infinite (%)0.0%
Mean12.434802
Minimum2
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-27T10:40:32.873431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17
median9
Q312
95-th percentile27
Maximum60
Range58
Interquartile range (IQR)5

Descriptive statistics

Standard deviation9.0840534
Coefficient of variation (CV)0.73053465
Kurtosis5.6343844
Mean12.434802
Median Absolute Deviation (MAD)2
Skewness2.2773818
Sum10523386
Variance82.520026
MonotonicityNot monotonic
2025-03-27T10:40:32.985479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 111789
10.7%
8 111753
10.7%
7 93993
9.0%
10 93034
8.9%
6 65607
 
6.3%
11 64454
 
6.1%
5 38422
 
3.7%
12 37214
 
3.5%
23 23567
 
2.2%
24 23378
 
2.2%
Other values (47) 183074
17.5%
(Missing) 202247
19.3%
ValueCountFrequency (%)
2 2609
 
0.2%
3 7492
 
0.7%
4 18510
 
1.8%
5 38422
 
3.7%
6 65607
6.3%
7 93993
9.0%
8 111753
10.7%
9 111789
10.7%
10 93034
8.9%
11 64454
6.1%
ValueCountFrequency (%)
60 38
 
< 0.1%
59 73
 
< 0.1%
58 105
 
< 0.1%
57 180
 
< 0.1%
56 326
 
< 0.1%
55 429
 
< 0.1%
54 675
0.1%
53 918
0.1%
52 1183
0.1%
51 1554
0.1%

Approval to Vendor Selection Days
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)< 0.1%
Missing233977
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean12.522979
Minimum2
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-27T10:40:33.110586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17
median9
Q315
95-th percentile26
Maximum60
Range58
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.8732403
Coefficient of variation (CV)0.62870348
Kurtosis3.5165425
Mean12.522979
Median Absolute Deviation (MAD)2
Skewness1.6787273
Sum10200655
Variance61.987913
MonotonicityNot monotonic
2025-03-27T10:40:33.221947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 102244
9.8%
8 102064
9.7%
7 85057
 
8.1%
10 84694
 
8.1%
6 59609
 
5.7%
11 58698
 
5.6%
5 34854
 
3.3%
12 33850
 
3.2%
23 31745
 
3.0%
24 31635
 
3.0%
Other values (47) 190105
18.1%
(Missing) 233977
22.3%
ValueCountFrequency (%)
2 2309
 
0.2%
3 6706
 
0.6%
4 17116
 
1.6%
5 34854
 
3.3%
6 59609
5.7%
7 85057
8.1%
8 102064
9.7%
9 102244
9.8%
10 84694
8.1%
11 58698
5.6%
ValueCountFrequency (%)
60 12
 
< 0.1%
59 23
 
< 0.1%
58 48
 
< 0.1%
57 50
 
< 0.1%
56 86
 
< 0.1%
55 162
 
< 0.1%
54 239
< 0.1%
53 349
< 0.1%
52 420
< 0.1%
51 540
0.1%

Vendor Selection to Acceptance Days
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)< 0.1%
Missing284452
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean17.060108
Minimum2
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-27T10:40:33.343899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median11
Q324
95-th percentile47
Maximum60
Range58
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.650349
Coefficient of variation (CV)0.74151638
Kurtosis0.89164051
Mean17.060108
Median Absolute Deviation (MAD)5
Skewness1.315442
Sum13035287
Variance160.03134
MonotonicityNot monotonic
2025-03-27T10:40:33.548050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 76450
 
7.3%
8 76402
 
7.3%
7 64313
 
6.1%
10 63454
 
6.1%
6 44779
 
4.3%
11 43997
 
4.2%
23 35812
 
3.4%
24 35810
 
3.4%
22 30201
 
2.9%
25 29512
 
2.8%
Other values (47) 263350
25.1%
(Missing) 284452
27.1%
ValueCountFrequency (%)
2 1710
 
0.2%
3 5267
 
0.5%
4 12781
 
1.2%
5 25945
 
2.5%
6 44779
4.3%
7 64313
6.1%
8 76402
7.3%
9 76450
7.3%
10 63454
6.1%
11 43997
4.2%
ValueCountFrequency (%)
60 97
 
< 0.1%
59 188
 
< 0.1%
58 332
 
< 0.1%
57 562
 
0.1%
56 864
 
0.1%
55 1287
 
0.1%
54 1829
0.2%
53 2516
0.2%
52 3338
0.3%
51 4173
0.4%

Acceptance to Installation Days
Real number (ℝ)

High correlation  Missing 

Distinct163
Distinct (%)< 0.1%
Missing356903
Missing (%)34.0%
Infinite0
Infinite (%)0.0%
Mean24.013689
Minimum2
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-27T10:40:33.671923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q119
median23
Q326
95-th percentile49
Maximum176
Range174
Interquartile range (IQR)7

Descriptive statistics

Standard deviation14.256826
Coefficient of variation (CV)0.59369577
Kurtosis8.9037996
Mean24.013689
Median Absolute Deviation (MAD)3
Skewness2.1594743
Sum16608564
Variance203.25708
MonotonicityNot monotonic
2025-03-27T10:40:33.781801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 71722
 
6.8%
24 71007
 
6.8%
22 60127
 
5.7%
25 59119
 
5.6%
21 41617
 
4.0%
26 41029
 
3.9%
8 26932
 
2.6%
9 26522
 
2.5%
20 24585
 
2.3%
27 23692
 
2.3%
Other values (153) 245277
23.4%
(Missing) 356903
34.0%
ValueCountFrequency (%)
2 630
 
0.1%
3 1896
 
0.2%
4 4490
 
0.4%
5 9185
 
0.9%
6 15681
1.5%
7 22407
2.1%
8 26932
2.6%
9 26522
2.5%
10 22201
2.1%
11 15679
1.5%
ValueCountFrequency (%)
176 2
< 0.1%
174 1
 
< 0.1%
173 1
 
< 0.1%
172 1
 
< 0.1%
171 2
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
167 3
< 0.1%
166 3
< 0.1%
165 3
< 0.1%

Installation to Inspection Days
Real number (ℝ)

High correlation  Missing 

Distinct172
Distinct (%)< 0.1%
Missing436530
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean27.664589
Minimum2
Maximum178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-27T10:40:33.905882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q113
median23
Q328
95-th percentile81
Maximum178
Range176
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.649017
Coefficient of variation (CV)0.74640605
Kurtosis4.921918
Mean27.664589
Median Absolute Deviation (MAD)6
Skewness1.9822545
Sum16930784
Variance426.3819
MonotonicityNot monotonic
2025-03-27T10:40:34.023734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 51666
 
4.9%
24 51532
 
4.9%
22 43661
 
4.2%
25 43083
 
4.1%
21 30406
 
2.9%
26 29699
 
2.8%
8 26194
 
2.5%
9 26087
 
2.5%
10 21740
 
2.1%
7 21674
 
2.1%
Other values (162) 266260
25.4%
(Missing) 436530
41.6%
ValueCountFrequency (%)
2 582
 
0.1%
3 1699
 
0.2%
4 4322
 
0.4%
5 9086
 
0.9%
6 15432
1.5%
7 21674
2.1%
8 26194
2.5%
9 26087
2.5%
10 21740
2.1%
11 14943
1.4%
ValueCountFrequency (%)
178 2
 
< 0.1%
176 2
 
< 0.1%
175 1
 
< 0.1%
174 2
 
< 0.1%
173 1
 
< 0.1%
172 6
< 0.1%
171 3
 
< 0.1%
170 3
 
< 0.1%
169 3
 
< 0.1%
168 9
< 0.1%

Inspection to Subsidy Redeemed Days
Real number (ℝ)

High correlation  Missing 

Distinct165
Distinct (%)< 0.1%
Missing523090
Missing (%)49.9%
Infinite0
Infinite (%)0.0%
Mean30.546022
Minimum2
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-27T10:40:34.127042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q121
median25
Q344
95-th percentile56
Maximum177
Range175
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.530587
Coefficient of variation (CV)0.63938235
Kurtosis3.8505125
Mean30.546022
Median Absolute Deviation (MAD)14
Skewness1.5050302
Sum16050163
Variance381.44385
MonotonicityNot monotonic
2025-03-27T10:40:34.252573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 38425
 
3.7%
24 38319
 
3.7%
22 32367
 
3.1%
25 31808
 
3.0%
21 22517
 
2.1%
26 22244
 
2.1%
8 17662
 
1.7%
9 17399
 
1.7%
7 14856
 
1.4%
10 14645
 
1.4%
Other values (155) 275200
26.2%
(Missing) 523090
49.9%
ValueCountFrequency (%)
2 398
 
< 0.1%
3 1241
 
0.1%
4 2967
 
0.3%
5 6185
 
0.6%
6 10256
1.0%
7 14856
1.4%
8 17662
1.7%
9 17399
1.7%
10 14645
1.4%
11 10076
1.0%
ValueCountFrequency (%)
177 2
 
< 0.1%
176 1
 
< 0.1%
175 2
 
< 0.1%
174 2
 
< 0.1%
173 1
 
< 0.1%
172 4
 
< 0.1%
171 5
< 0.1%
169 5
< 0.1%
168 11
< 0.1%
167 6
< 0.1%

Subsidy Redeemed to Released Days
Real number (ℝ)

High correlation  Missing 

Distinct176
Distinct (%)< 0.1%
Missing611177
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean28.417352
Minimum2
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-27T10:40:34.373721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q120
median24
Q339
95-th percentile53
Maximum181
Range179
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.475539
Coefficient of variation (CV)0.6853397
Kurtosis10.695776
Mean28.417352
Median Absolute Deviation (MAD)5
Skewness2.5047558
Sum12428471
Variance379.29664
MonotonicityNot monotonic
2025-03-27T10:40:34.486510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 38483
 
3.7%
24 38328
 
3.7%
22 32339
 
3.1%
25 31533
 
3.0%
21 23022
 
2.2%
26 21888
 
2.1%
8 14679
 
1.4%
9 14409
 
1.4%
20 13313
 
1.3%
27 12620
 
1.2%
Other values (166) 196741
 
18.8%
(Missing) 611177
58.3%
ValueCountFrequency (%)
2 334
 
< 0.1%
3 928
 
0.1%
4 2497
 
0.2%
5 5051
 
0.5%
6 8589
0.8%
7 12264
1.2%
8 14679
1.4%
9 14409
1.4%
10 12303
1.2%
11 8488
0.8%
ValueCountFrequency (%)
181 1
 
< 0.1%
180 3
 
< 0.1%
179 2
 
< 0.1%
178 3
 
< 0.1%
177 2
 
< 0.1%
176 5
< 0.1%
175 3
 
< 0.1%
174 7
< 0.1%
173 10
< 0.1%
172 12
< 0.1%

Interactions

2025-03-27T10:40:18.425325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:06.627205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:08.491814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:10.239064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:11.958067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:13.723704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:15.346394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:16.909948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:18.692480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:06.910491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:08.723254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:10.456903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:12.187431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:13.941875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:15.541862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:17.103645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:18.877272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:07.160941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:08.962940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:10.692332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:12.507186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:14.141865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:15.741638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:17.275850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:19.051953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:07.394038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:09.191541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:10.925225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:12.709903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:14.373043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:15.942712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:17.486295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:19.225893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:07.609997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:09.409867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:11.142680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:12.925846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:14.574114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:16.156893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:17.659744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:19.405317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:07.825634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:09.609862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:11.347047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:13.125880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:14.774918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:16.358629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:17.864612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:19.575139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:08.034087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:09.809625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:11.542833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:13.325264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:14.969809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:16.542690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:18.041717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:19.752997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:08.228020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:09.993968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:11.728366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:13.508666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:15.143095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:16.721986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-27T10:40:18.242324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-27T10:40:34.580761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Acceptance StatusAcceptance to Installation DaysApplication NumberApproval to Vendor Selection DaysGenderInspection to Subsidy Redeemed DaysInstallation to Inspection DaysProduction Capacity (KW)RWA/ResidentialRegistration to Approval DaysState/UTSubsidy Redeemed to Released DaysVendor OrganizationVendor Selection to Acceptance Days
Acceptance Status1.0001.0000.0001.0000.0021.0001.0000.0000.0041.0000.1691.0000.0001.000
Acceptance to Installation Days1.0001.0000.0000.0010.003-0.008-0.0040.0000.0020.0010.002-0.0110.0010.001
Application Number0.0000.0001.0000.0010.0010.0020.0010.0010.0020.0020.0010.0020.000-0.000
Approval to Vendor Selection Days1.0000.0010.0011.0000.001-0.004-0.0030.0000.002-0.0010.002-0.0050.001-0.001
Gender0.0020.0030.0010.0011.0000.0020.0020.0010.0010.0010.0000.0000.0000.000
Inspection to Subsidy Redeemed Days1.000-0.0080.002-0.0040.0021.000-0.0150.0020.002-0.0040.002-0.0180.000-0.008
Installation to Inspection Days1.000-0.0040.001-0.0030.002-0.0151.0000.0000.000-0.0030.000-0.0160.001-0.005
Production Capacity (KW)0.0000.0000.0010.0000.0010.0020.0001.0000.0000.0010.0020.0030.0000.000
RWA/Residential0.0040.0020.0020.0020.0010.0020.0000.0001.0000.0010.0810.0050.0000.001
Registration to Approval Days1.0000.0010.002-0.0010.001-0.004-0.0030.0010.0011.0000.000-0.0080.001-0.000
State/UT0.1690.0020.0010.0020.0000.0020.0000.0020.0810.0001.0000.0030.0000.000
Subsidy Redeemed to Released Days1.000-0.0110.002-0.0050.000-0.018-0.0160.0030.005-0.0080.0031.0000.002-0.014
Vendor Organization0.0000.0010.0000.0010.0000.0000.0010.0000.0000.0010.0000.0021.0000.000
Vendor Selection to Acceptance Days1.0000.001-0.000-0.0010.000-0.008-0.0050.0000.001-0.0000.000-0.0140.0001.000

Missing values

2025-03-27T10:40:20.438618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-27T10:40:22.094977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-27T10:40:25.374750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Application NumberGenderState/UTDistrictRWA/ResidentialDiscom NameAcceptance StatusProduction Capacity (KW)Vendor OrganizationRegistration DateApplication Approved DateVendor Selection DateVendor Acceptance DateInstallation DateInspection DateSubsidy Redeemed DateSubsidy Released DateRegistration to Approval DaysApproval to Vendor Selection DaysVendor Selection to Acceptance DaysAcceptance to Installation DaysInstallation to Inspection DaysInspection to Subsidy Redeemed DaysSubsidy Redeemed to Released Days
077442871FemaleMizoramLungleiResidentialNational Thermal Power Corporation (NTPC)Accepted3 - 4 KWGreenSpark Solar2024-11-032024-11-102024-11-212024-11-302024-12-23PendingPendingPending8.012.010.024.0NaNNaNNaN
146760652FemaleMaharashtraNandedResidentialMaharashtra State Electricity Distribution Company Limited (MSEDCL)Accepted3 - 4 KWGreenSpark Solar2024-02-082024-02-132024-03-212024-04-152024-05-312024-06-242024-07-122024-09-046.038.026.047.025.019.055.0
250016740FemaleMadhya PradeshSehoreResidentialMadhya Pradesh Poorv Kshetra Vidyut Vitaran Company LimitedAccepted3 - 4 KWSolarPeak Innovations2024-04-052024-04-152024-04-212024-04-262024-05-162024-06-282024-07-212024-08-1211.07.06.021.044.024.023.0
313098830MaleAndhra PradeshNelloreResidentialAndhra Pradesh Central Power Distribution Company LimitedAccepted4 - 5 KWPowerSun Technologies2024-09-272024-10-032024-10-112024-10-162024-11-062024-11-28PendingPending7.09.06.022.023.0NaNNaN
462980262FemaleBiharKhagariaRWANorth Bihar Power Distribution Company Limited (NBPDCL)Accepted3 - 4 KWSolarCrest Enterprises2024-06-292024-07-082024-07-142024-07-222024-08-142024-09-022024-09-252024-10-1510.07.09.024.020.024.021.0
550072448MaleMaharashtraHingoliResidentialMaharashtra State Electricity Distribution Company Limited (MSEDCL)Accepted3 - 4 KWBrightSun Power2024-07-142024-07-182024-07-292024-09-212024-10-012024-11-132024-12-222024-12-315.012.055.011.044.040.010.0
625621825MaleSikkimEast SikkimRWAPowerGrid Corporation of IndiaAccepted3 - 4 KWIlluminateSun Technologies2024-12-172024-12-24PendingPendingPendingPendingPendingPending8.0NaNNaNNaNNaNNaNNaN
760826911MaleOdishaKandhamalResidentialSouthern Electricity Supply Company of Odisha (SOUTHCO)Accepted3 - 4 KWSolarHarvest Energy2024-03-122024-04-032024-04-212024-05-022024-05-082024-06-242024-08-062024-08-2923.019.012.07.048.044.024.0
813990366FemaleAndhra PradeshSrikakulamResidentialAndhra Pradesh Eastern Power Distribution Company LimitedAccepted3 - 4 KWGreenSpark Solar2024-07-012024-07-092024-07-292024-08-252024-09-032024-09-142024-10-092024-11-019.021.028.010.012.026.024.0
963207356FemaleTamil NaduMayiladuthuraiResidentialTamil Nadu Generation and Distribution Corporation Limited (TANGEDCO)Accepted3 - 4 KWSolarCrest Enterprises2024-03-282024-04-082024-04-122024-04-182024-05-252024-06-152024-07-292024-09-0912.05.07.038.022.045.043.0
Application NumberGenderState/UTDistrictRWA/ResidentialDiscom NameAcceptance StatusProduction Capacity (KW)Vendor OrganizationRegistration DateApplication Approved DateVendor Selection DateVendor Acceptance DateInstallation DateInspection DateSubsidy Redeemed DateSubsidy Released DateRegistration to Approval DaysApproval to Vendor Selection DaysVendor Selection to Acceptance DaysAcceptance to Installation DaysInstallation to Inspection DaysInspection to Subsidy Redeemed DaysSubsidy Redeemed to Released Days
104852254548938FemaleRajasthanBaranRWAJodhpur Vidyut Vitran Nigam Limited (JdVVNL)Rejected3 - 4 KWIlluminateSun Technologies2024-03-08DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
104852396025358FemaleUttar PradeshMirzapurRWALucknow Electricity Supply Administration (LESA), Lucknow CityAccepted2 - 3 KWGreenSpark Solar2024-12-012024-12-102024-12-14PendingPendingPendingPendingPending10.05.0NaNNaNNaNNaNNaN
104852464655782FemaleTamil NaduTenkasiRWATamil Nadu Generation and Distribution Corporation Limited (TANGEDCO)Accepted5 - 6 KWGreenSpark Solar2024-03-182024-03-272024-04-012024-04-062024-05-202024-06-092024-07-242024-08-1310.06.06.045.021.046.021.0
104852594327640MaleMaharashtraParbhaniResidentialTata PowerAccepted3 - 4 KWSolarEdge Systems2024-11-182024-11-232024-11-302024-12-25PendingPendingPendingPending6.08.026.0NaNNaNNaNNaN
104852652084659FemaleMadhya PradeshSehoreResidentialMadhya Pradesh Poorv Kshetra Vidyut Vitaran Company LimitedAccepted4 - 5 KWPowerSun Technologies2024-02-252024-03-042024-03-082024-04-022024-04-232024-07-122024-08-262024-11-239.05.026.022.081.046.090.0
104852769494107FemaleTelanganaNagarkurnoolResidentialNorthern Power Distribution Company of Telangana Limited (TSNPDCL)Accepted3 - 4 KWRadiantSun Energy2024-02-182024-02-272024-03-052024-04-162024-05-232024-06-082024-06-272024-07-1710.08.043.038.017.020.021.0
104852863201035FemaleMeghalayaWest Khasi HillsResidentialPowerGrid Corporation of IndiaAccepted3 - 4 KWSunBeam Energy Solutions2024-11-042024-11-102024-11-162024-11-242024-12-14PendingPendingPending7.07.09.021.0NaNNaNNaN
104852962238278FemaleUttar PradeshBhadohiRWAPashchimanchal Vidyut Vitran Nigam Limited (PVVNL), Meerut ZoneRejected3 - 4 KWInfiniteLight Solar2024-01-26DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
104853011048201FemaleGujaratAravalliResidentialTorrent Power Limited, AhmedabadRejectedAbove 6 KWSunWave Energy2024-02-17DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
104853187559218MaleGujaratMehsanaResidentialMadhya Gujarat Vij Company Limited (MGVCL), VadodaraRejected3 - 4 KWSolarEdge Systems2024-12-13DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN